1. Field of the Invention
This invention pertains generally to image processing and acquisition, and more particularly to blur difference estimation mechanisms.
2. Description of Related Art
The estimation of blur difference between captured images can be a fundamental component within a variety of image capture and processing apparatus, such as toward estimating actual subject depth. Cameras and other optical devices can utilize blur differences between captured images of a subject to estimate depth and control focus. For example a camera may utilize present and previous depth estimations for the picture to improve focus control.
Blur change is modeled as a point spread function which may be approximated by a series of convolutions involving a blur kernel. Suppose fA and fB are two pictures captured at two different focus positions, with fA being sharper then fB. Let K be the blur kernel. The blur difference between the two pictures, in terms of number of convolutions, is given by:
      I    A_B    =      argmin    ⁢                                              f            A                    ⁢                                    *              K              *              K              *              …              *              K                                      ︸                              I                ⁢                                                                  ⁢                Convolutions                                                    -                  f          B                          
The blur difference between the two pictures in terms of variance is IA—Bσ2, where σ2 is the variance of the blur kernel K. However, there are known issues and tradeoffs which arise when performing this convolution. (1) The number of convolutions required is inversely proportional to the variance of the kernel. (2) If using a kernel with a large variance, the blur difference estimation is acquired with fewer convolutions, but at a low accuracy. (3) If using a kernel with a small variance, additional computational cost is involved, yet the resultant blur difference estimation has a higher accuracy.
Accordingly, a need exists for an apparatus and method which is capable of overcoming the convolution issues and associated tradeoffs so that blur differences can be obtained more rapidly without sacrificing accuracy.